Corpus-driven genitive disambiguation

Corpus-driven genitive disambiguation
Nadine Aldinger
University of Stuttgart, IMS
Azenbergstr. 12, D-70174 Stuttgart
This paper describes work in progress on the corpus-based acquisition of
morphosyntactic and lexical-semantic context parameters to disambiguate genitive
attributes of German deverbal nouns.
Nominalizations and genitive attributes
In my Ph.D. dissertation project I develop methods for semi-automatic text analysis to
disambiguate genitive attributes of German deverbal nouns, especially those ending in
–ung, like Verfolgung “persecution”.
The suffix –ung is used to derive feminine nouns (mostly) from transitive verbs. It
shares an etymological root with English –ing but forms full nouns. The meaning of –
ung is comparable to that of the latinate suffix –(a)tion.
As with many relational nouns – e.g. picture in Picasso’s picture –, genitive attributes
of –ung nominals can be subject (agent-related, 1) or object (theme-related, 2)
(1) a.
Dort ist die Radioaktivität laut
there is the radioactivity according-to measurements
Organisation hundertmal
höher als normal.
the-GEN organization a-hundred-times higher than normal
“According to measurings of/by the organization, the radioactivity level is
a hundred times higher there.”
Der Satellit dient der Messung
von Verschiebungen in der
The satellite serves the-DAT measurement of shifts
in the
“The satellite serves to measure shifts in the earth’s crust.”
In (1a), the genitive der Organisation “of the organisation” denotes the role-player
who measures something (the radioactivity level), i.e. the thematic agent and
grammatical subject of the nominal’s base verb messen “to measure”; in (1b), the
genitive von Verschiebungen “of shifts” denotes the thing measured, i.e. the theme
and grammatical object of the base verb. At the structural surface, both interpretations
are possible in both cases.
I do not distinguish between “genuine” subject genitive and author’s genitive, as
Ehrich/Rapp (2000) do; for NLP-related work, the important matter is the semantic
relatedness of the genitive attribute to the agent/subject/causer of the
event/state/process characterized by the nominal’s base verb.
There are also non-thematic genitives, e.g. temporal (die Lieferungen dieses Monats
“the deliveries of this month”) and modal genitives (Lieferungen dieser Art
“deliveries of this kind”). They are not restricted to nominalizations and therefore will
not be treated further in this paper, although they must of course be detected and
filtered out in the disambiguation process. See section 4.2 for a list.
Human listeners use structural and semantic clues as well as world knowledge to
disambiguate genitive attributes – e.g. in (1a) the knowledge that organizations more
probably measure things than they themselves are measured. Automatic text analyzers
for deep NLP also need to disambiguate genitive attributes, in order to provide input
for information extraction and information retrieval. But they do not have a very
elaborate “understanding” of the world (yet); in particular, they do not possess the
world knowledge that humans have at their hands to determine the interpretation
intended. Therefore it is a challenge to find clues for genitive disambiguation which
can be assessed by computational tools.
Usable features may be morphosyntactic (a,b) or lexical-semantic (c,d):
a. morphosyntactic form of the DP headed by the nominal: i.e. number and
definiteness, maybe case
b. syntactic structure of the local context: inner structure of the nominal’s DP,
but also embedding in PPs/VPs
c. properties of the nominal’s base verb: e.g. telicity, syntactic subcategorization
d. lexical material in the local context: e.g. selectional restrictions of the
governing verb
One can look for the most useful disambiguation clues from these categories in
theories about nominalizations and their genitive attributes, generalizing that an
interpretation will never occur in contexts in which it is not allowed according to
theory. This approach has its drawbacks: the subtle semantic notions which linguistic
theory uses are often hard to observe in real text. Therefore I decided to extract
context parameters for genitive disambiguation directly from pre-annotated corpus
text. If a text analyzer checks for such parameters when it encounters an –ung noun
with a genitive attribute in corpus text, it should be able (ideally) to give the correct
interpretation, or (in the real world) to give a weighted guess – here exemplified for
the sentences (1a,b):
In (1a), there is a plural head nominal Messungen “measurements”; the whole
NP is embedded in a PP with the preposition laut “according to”. Additionally,
the head lemma of the genitive, Organisation “organization”, can be classified
as a collective noun even in a very shallow semantic analysis, e.g. using the
German WordNet equivalent, GermaNet (Kunze et al. 2003).
According to preliminary analyses, these three parameters – plural nominal,
PP-embedding with laut, and collective nouns as head lemma of the genitive
attribute – occur significantly more often with subject genitives than with
object genitives. Therefore they are good candidates to detect subject genitives.
In (1b), on the other hand, the NP headed by the nominal Messung is the
indirect object of the verb dienen “to serve”, which suggests that the subject is
the actor of the nominalization. Together with the morphosyntactical features
of the NP – definite singular – and the indefiniteness of the genitive attribute,
this indicates an object genitive.
The procedure I adopted to find and evaluate context parameters is outlined in section
2. Since my work not only aims at applications, but has some perspective on linguistic
theory as well, it relies on semi-automatic, symbolic acquisition methods (aided, but
not determined by statistics) rather than on statistically induced machine learning.
I am now building a database out of corpus examples for nominalizations and genitive
attributes and annotate context parameter values (automatically) and genitive
interpretation (manually) for each example. The corpus is morphologically annotated
and chunked (see section 4.1).
1. Collect linguistic context parameters that might be relevant for genitive
interpretation from literature and from (qualitative) corpus observations.
2. Collect corpus sentences containing representative nominalizations plus
genitive attributes in a database and annotate their parameter values
3. Annotate genitive interpretation manually for each sentence.
4. Analyze frequency distributions to find the parameters and parameter
combinations that are most useful to predict genitive interpretation.
5. Implement tests based on these combinations.
6. Re-run the tests on new corpus sentences and evaluate the quality of their
predictions for genitive interpretation; if necessary and possible, improve the
tests and look for more diagnostic parameters (bootstrapping).
In this paper, I will concentrate on steps 1 to 4. Section 4 deals with data collection
and annotation for the example database, and section 5 presents some first evaluations.
State of the art
Some descriptive and theoretical work has been done on the interpretation of genitive
attributes of nominals and their relation to base verb arguments. Two recent accounts
are Ehrich/Rapp (2000) and Ehrich (2002). Taking a lexical-semantic approach, they
generalize on the (un)grammaticality of certain genitive interpretations in combination
with certain parameters:
sortal reading of the nominal (“sort” here defined as top-level entity class
related to Aktionsart – the basic sorts are Process, Event, (resultant) State or
(physical) Object): Process nominals can take subject genitives, Object
nominals (generally) cannot.
telicity and related lexical-semantic properties of the base verb: atelic
(Process) nominals can take subject genitives, telic (Event) nominals cannot.
number of the nominal: some plural telic nominals can take subject genitives
under special semantic circumstances, singular telic nominals cannot.
The first two parameters in this list are only very indirectly observable in corpora,
therefore they are of limited use in automatic extraction tasks. Ehrich and Rapp
confine themselves almost completely to artificially constructed examples and do not
use their generalizations to predict genitive interpretation in real data. Hardly
surprising, empirical analysis reveals that their general predictions are borne out in
some cases but fail in others.
A more comprehensive study of diagnostic features for genitive interpretation has, to
my knowledge, never been carried out. Especially the clues that embedding structures
(PPs, VPs) provide for the interpretation of genitives (or indeed, any syntactic
construction) have not been investigated yet, neither from a theoretical nor from a
practical viewpoint.
In work on extraction of linguistic information from corpora (in general) and
disambiguation (in particular), there seem to be very few approaches to exploit
context parameters at present.
Spranger (2004) exploits the extraction of context parameters for chunking; the
analysis of linguistic context parameters to detect collocations and idiomatic
expressions has been proposed by Evert et al. (2004).
Concerning ambiguities, precision-oriented (Eckle-Kohler 1999) and recall-oriented
(Schulte im Walde 2002) approaches are to be distinguished. Eckle-Kohler’s grammar
is specialized on the extraction of verb subcategorization frames and analyzes only
unambiguous sentences. This yields a precision of 70-80%, but only 2% recall and
leads to a need for very large amounts of text. Recall-oriented approaches mostly
build on statistical methods; they have to accept ambiguous output and are often
restricted to a coarse classification of the phenomena they extract.
Data collection and annotation
Corpus architecture
The nominals and attributes for the example database are extracted from a German
newspaper corpus (Frankfurter Rundschau 1992-93) with a size of about 40 million
words. The corpus is
lemmatized and part-of-speech tagged (using the STTS tagset, Schiller et al.
1999) with TreeTagger (Schmid 1994),
morphosyntactically annotated
and chunked with YAC (Kermes 2003).
The IMS Corpus Workbench (, which is used to work with the corpus,
provides a regular-expression query language, CQP (on-line demos at, and a Perl
interface to operate on the query results. I employ the latter to annotate values of
specified context parameters to the query results and store them in a MySQL database
(see section 4.2;
As an inflectional language, German displays a large amount of case/number
syncretism. The morphosyntactic annotation in the corpus preserves the case/number
ambiguities of nouns, adjectives, pronouns, and determiners by annotating sets of
case/number/definiteness triples rather than single values or value sets. The chunker,
YAC, reduces the ambiguities as far as possible on the chunk and phrase level by
assigning to noun chunks and noun phrases the intersection of the sets of
case/number/definiteness triples of their sub-constituents. Building on the
disambiguated annotation, the chunker can attach post-nominal genitive NPs as
attributes with reasonable precision.
Database layout
The database consists of three tables:
nominals: complete list of all -ung nominals (with or without genitive) in the
corpus, including compounds
verbs: subcategorization information about the nominals’ base verbs
matches: all corpus matches of nominal + genitive attribute, annotated with
context parameters
First, a list of all –ung nominals in the corpus was compiled with a simple corpus
[pos="NN" & lemma=".+ung"]
After semi-manual deletion of false matches like Zeitung “newspaper” (not analyzable
as a deverbal noun in present-day German) or Sprung “jump” (a non-deverbal noun
accidentally ending in –ung), 36077 nominal lemmas were stored in the database,
together with their occurrence frequency in the corpus.
Compounding is a very productive word-formation process in German, and the
database contains many compound nominals – e.g. 31 compounds with Abdeckung
“covering, cover” as head, including Fahrbahnabdeckung “cover(ing) of a road”,
Glasabdeckung “glass cover”, Winterabdeckung “cover(ing) for the winter” and
Vollabdeckung “full cover(ing)”. This selection illustrates the variety of ways in
which head and non-head(s) of a compound can be related.
Non-heads – i.e. the left parts – of compound nominals provide at least one useful
clue for genitive disambiguation (as well as valuable information about compounding
in general): If the non-head refers to the direct object of the nominal’s base verb as in
Fahrbahnabdeckung, a post-nominal genitive attribute cannot be object genitive. To
exploit this, I used the SMOR morphological analyzer (Schmid et al. 2004) to detect
compound nominals and store their head lemma and non-head part separately.
Subcategorization information for the base verbs is taken from an automatically
compiled and manually corrected verb lexicon (Eckle-Kohler 1999). Verbs may have
multiple subcategorization frames, some of which distinguish different verb meanings.
Not all of these go into –ung nominal formation – e.g., Wartung can only be related to
transitive etw. warten “to maintain (a machine)”, not to the much more frequent
intransitive auf jdn. warten “to wait for s.o.”. Optimally, all nominals should be
related to verb meanings in the database, not just to single verbs.
For a more detailed description of the table of matches, see sections 4.3 and 4.4.
Extraction procedure
The extraction query for nominal + genitive attribute exploits the genitive attribute
attachment performed by YAC (see section 4.1). In addition, the query extracts some
PPs headed by the preposition von “of, by” as “pseudo-genitive” attributes. The
reason is that, in German, there is no way to express an unmodified indefinite plural
genitive – except by paraphrasing it with a von-PP governing dative case (2b). There
is no indefinite plural determiner in German, so an unmodified indefinite plural
genitive DP (2a) would leave the noun’s case and number unspecified – regarding
morphology alone, the noun (here Institute) could also be e.g. nominative singular.
Definite plural genitive DPs are grammatical (2c) because here genitive case is
marked on the determiner; and indefinite plural genitive DPs with an adjectival
modifier (2d), which mark genitive case on the adjective, are grammatical as well.
Thus, modified indefinite plural genitives can be distinguished from von-PPs with a
modified inner NP (2e) – so the latter should (probably) not be treated as genitive
(2) a.
* die Messungen
the measurements institutes-GEN(?)
(indef. pl. without modifier)
die Messungen
von Instituten
the measurements of institutes-DAT
(indef. pl. without modifier)
die Messungen
the measurements the-GEN institutes-GEN
die Messungen
bekannter Institute
the measurements famous-GEN institutes-GEN
die Messungen
von bekannten Instituten
the measurements of famous-DAT institutes-DAT
(def. pl.)
(indef. pl. with modifier)
(“real” von-PP)
As the chunker does not attach PPs at all, post-nominal von-PPs have to be extracted
separately. The query excludes von-PPs with an article or adjectival modifier after the
preposition, like (2e) or its definite counterpart, von den bekannten Instituten “of the
famous institutes”.
To fill the table of matches, nominals from base verbs with certain selectional
properties (e.g. verbs selecting for a prepositional or propositional object) are marked
with a flag in the nominals table, including compounds. If the group is very large,
only the nominals with the 20 most frequent heads are marked. Then a Perl script is
invoked which collects all occurrences of the marked nominal + genitive attribute via
CQP, automatically annotates their values of the specified context parameters and
stores them (as whole sentences) in the database.
(3a,b) are two simplified examples as extracted from the corpus:
(3) a.
der Gewinn, den
sie durch
Vermietung ganzer
the profit which they through renting
whole-GEN floors-GEN to
polnische Leiharbeiter
erzielt hatten
casual workers made
“the profit they had made by renting whole floors to Polish casual
Die Bodenmessungen des
The soil measurings the-GEN municipal-GEN environmental authority
ergaben katastrophale Ergebnisse.
yielded disastrous
“The soil measurings of the municipal environmental authority yielded
disastrous results.”
Table 1 shows the context parameters currently annotated and extensions planned for
the near future (see also section 7). To exemplify the annotation, the complete
automatically generated annotations for (3a,b) are shown in the last two columns.
specifier: word
specifier: part of
speech (STTS
post-genitival PP:
Features of the nominal and its NP
sg, pl
def, indef, null (bare
(set of) nom, gen, dat, acc nom, gen,
dat, acc
ART (article: die, eine)
PDAT (demonstrative
pronoun: diese, …)
PIAT (indefinite pronoun:
keine, etwas, …)
PPOSAT (possessive
pronoun: seine, ihre, …)
NE (proper name bearing
genitive case)
nom, acc
post-genitival PP:
case of governed NP
for compounds: nonhead
(set of) gen, dat, acc
Features of the genitive NP / PP
sg, pl, pp-von
def, indef, null (bare
head lemma
animacy and/or other STRING
general lexical
properties from
GermaNet (Kunze et
al. 2003)*
Features of embedding context
preposition of
embedding PP
main verb lemma of STRING
the clause in which
the nominal’s NP is
an argument or
subject, direct object,
function of the
adjunct …
nominal’s NP w.r.t.
the clause verb*
* planned
Table 1. Automatically annotated context parameters
Corpus annotation of number and definiteness is not always reliable – therefore I use
morphology and especially the forms and lemmata of determiners to retrieve this
information directly from the corpus. Since this procedure needs five different CQP
templates (stored queries) to search for definite singular, definite plural, indefinite
singular, indefinite plural and null singular nominal NPs, the values for number and
definiteness of the nominal’s NP can be inferred from the name of the invoked CQP
template. All other parameter values are not part of the query, but are determined by
Classification of genitives
In the example database, genitive interpretation is labelled manually for each corpus
match. Frequency analyses of the annotation results will serve as basis to determine
the most useful context parameters to be implemented in the semi-automatic genitive
disambiguation tool.
The following genitive labels may be assigned:
subject genitive: die Befürchtung der Gewerkschaft “the fear of the trade
object genitive: die Befürchtung einer Finanzkrise “the fear of a financial
subject genitive with non-transitive base verb:
intransitive or unaccusative base verb – eine Erkrankung des Herzens “a
disease of the heart”, base erkranken (intr.) “to fall sick”;
inherently (or very preferably) reflexive verb – die Annäherung des Autos “the
approach of the car”, base verb sich annähern (refl.) “to approach”
other thematic genitives: in Ermangelung guter Alternativen “for lack of
good alternatives”, base verb ermangeln (dummy subject, genitive object or
an-PP) “to lack”
non-thematic genitives: temporal – die Lieferungen dieses Monats “the
deliveries of this month”; modal – die Lieferungen dieser Art “the deliveries of
this kind”; with superlative – die (größte) Entdeckung der Ausstellung “the
(biggest) discovery of the exhibition”; quantitative – eine Erhöhung von 3,5 %
“a raise by 3.5 %”
ambiguous instances:
ambiguity between subject and object genitive is rare;
more frequently: ambiguity, or rather vagueness, between subject and
reflexive-subject genitive, due to different verb readings – die Angleichung der
Lebensverhältnisse “the adjustment of the life standards”, base verb
angleichen (tr./refl.) “to adjust / o.s.”
genitive modifying the non-head of a compound nominal: die
Altersverteilung der Studenten “the age distribution of the students”
First results
In a preliminary study, 16 –ung nominals from the semantic verb classes given by
Ehrich/Rapp (2000) were taken as a test set. About 2000 corpus instances of these
nominals with genitive attributes were annotated semi-automatically with
morphosyntactic features, with the main purpose of testing Ehrich and Rapp's
predictions on real text.
It turned out that the subject-object genitive ratio for the 16 nominals was mostly
determined by the following three parameters:
number of the nominal (Verfolgung “persecution” vs. Verfolgungen
availability of Object reading for the nominal instance (i.e. whether the
nominal can denote a physical object) (Herstellung “production” vs.
Bearbeitung “revision”) – especially for mental Objects, i.e. “Object”
instances of nominals from proposition-embedding verbs (Äußerung
subcategorization properties of the nominal’s base verb
Partly contradicting Ehrich and Rapp’s position, (a)telicity of the nominals’ base
verbs does not seem to make much difference – except in indirect ways connected
with Object readings, which are generally unavailable for nominals from atelic verbs
because the verbal semantics of atelics does not imply a specified result state.
Evaluating the example database, I focus on these parameters in turn in the following
three sections, returning once more to the role of lexical semantics in section 5.4.
Singular vs. plural nominals
In the small test set, I followed Ehrich and Rapp’s approach by treating nominals from
telic and atelic base verbs separately.
For singular definite nominals from atelic verbs, like Verfolgung “persecution”, there
are almost no subject genitives (two of 78 total genitives). Likewise, there are no
subject genitives at all for the Process and Event instances of telic nominals like
Absperrung “blocking off”.
Pluralization of the nominal, however, reverses the relation – here subject genitives
predominate: e.g. for Absperrungen (pl.), five of six genitive attributes are subject
genitives. However, the plural nominals all have State or Object readings – so
Ehrich/Rapp (2000) would classify the genitive attributes as “author’s genitive” at
least for the Object nominals. Furthermore, there were no corpus matches at all (with
a genitive attribute) for several plural nominals from telic verbs (Herstellungen
“productions”, Errichtungen “establishings”, Unterstützungen “supports”,
Vernichtungen “annihilations”) – note that, for all of these, the Object reading is
In the large, automatically annotated example database, 24007 corpus matches with
1050 different nominals are currently annotated with genitive interpretations (200506-13). The data confirm the effects demonstrated in the preliminary study. Table 2
shows the subject-object genitive ratio for all number-definiteness combinations,
highlighting the influence of number. E.g., with definite singular nominals, there are
about 13 object genitives for each subject genitive, while with plural nominals we
have about three subject genitives for one object genitive.
gen.subj. gen.obj. others
Table 2. Genitive interpretation vs. number and definiteness of the head nominal in the example
Object readings
Some nominals, like Bearbeitung “revision”, Übersetzung “translation” and
Unterstützung “support”, show a significant number of instances with subject
genitives in singular; but these are almost exclusively Object nominals and fixed
constructions like in Bearbeitung von Hans Schmidt “in a revised version by Hans
Schmidt”. There are some interesting support verb constructions like Unterstützung
finden “find support” where only subject genitives may occur – whether this support
verb or others have the same effect on other nominals remains to investigate.
The difficult thing about Object readings is that they are hard to detect with corpuslinguistic methods. Heuristics for Object readings must rely heavily on semantics, e.g.
embedding of the nominal’s NP under spatial verbs (stand, build) and prepositions
(behind), or spatial modifiers (solid).
Subcategorization properties of base verbs
A more detailed analysis clearly requires a closer look at other properties of the
nominals involved, especially on the subcategorization (and perhaps thematic)
properties of the nominals’ base verbs. The most striking example I found so far is the
predictive power of proposition-embedding.
Befürchtung “fear”, from the proposition-embedding base verb befürchten “to fear”,
occurs almost only with subject genitives. The remaining object genitives themselves
denote Events or Processes – e.g. die Befürchtung regulierender
Studienverschärfungen “the fear of regulating tightenings of studying conditions”, or
less clearly die Befürchtung einer Finanzkrise “the fear of a financial crisis”.
The semantics of the nominal might explain this frequency distribution: Befürchtung
denotes a proposition that someone utters or has in mind: a kind of mental Object. The
subject genitive refers to the holder of the proposition, here the “fearer”.
This analysis can be extended to other verbs subcategorizing propositions. Examples
with similar genitive distribution are Äußerung “utterance” (base verb äußern “to
utter”), Mitteilung “announcement, notification” (base verb mitteilen “to
communicate”), and at least one sense of Feststellung “remark, observation, statement,
conclusion” (base verb feststellen “to remark”) and even Messung “measurement”
(base verb messen “to measure”), which at first glance does not have much to do with
proposition-embedding mental Objects.
Embedding context
Up to now, only embedding in PPs has been annotated and analyzed (for clausal
embedding and grammatical functions see section 7).
Strong biases of genitive interpretation in connection with certain prepositions are
probably in most cases idiosyncratic to single nominals or small semantic groups. In
combination with bare singular nominals, they often form fixed constructions like in
Bearbeitung von Hans Schmidt “in a revised version by Hans Schmidt” (see also
section 5.2) or auf Anweisung des Lehrers “on instruction from the teacher”.
The exemplary data analyses highlight some parameters in linguistic context which a
semi-automatic semantic text analyzer can use to disambiguate syntactic constructions
and their semantic interpretations. The most striking example from the database up to
now is the effect of number of the head nominal on the frequency of subject genitive
attributes; more complex trails to follow involve e.g. the influence of base verb
subcategorization properties. The first analyses demonstrate the possibilities of a
corpus-driven perspective on disambiguation of real text.
Next steps
In addition to enlarging the example database with more examples in the present state
of parameter annotation, the annotation will be extended to more complex parameters,
especially information about clausal context and grammatical function of the
nominal’s NP and lexical-semantic information from ontologies (e.g. GermaNet).
Since annotation of the former in a corpus would require full parsing, the main verb of
the clause containing the nominal’s NPs, and especially the grammatical function of
the NP with respect to the verb, will have to be annotated, or at least checked,
manually in most cases. I also plan to extend the annotation of subcategorization
parameters to thematic proto-roles in the sense of Dowty (1991) or Blume (2000).
Proto-roles split the “traditional” thematic roles (a.k.a. semantic cases) into subproperties like causation or movement. Yet again, these proto-roles would have to be
annotated manually for each verb, and I doubt that such properties can be applied
consistently on abstract verbs.
The use of ontologies will allow us to exploit some more heuristics from manual
genitive classification, e.g. the observation that subject genitives are much more
frequent with genitive attributes denoting a human, a group of humans or an
institution. Information from an ontological hierarchy may also help to identify Object
readings of nominals when they are grammatical subjects or objects of spatial verbs,
as in eine Absperrung errichten “erect a barrier”. Finally, ontological information
combined with information on grammatical functions helps to determine if the base
verb’s object (or subject) is already occupied by another constituent in the sentence
containing the nominal, ruling out the possibility that the nominal’s genitive attribute
also refers to this category.
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